David Silver

Howie Choset, Ioannis M. Rekleitis, David Silver, 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

Temporal-difference search in computer Go

Richard S. Sutton, David Silver, Martin Müller, 2012, Machine Learning.

Reinforced Variational Inference

David Silver, Nicolas Heess, John Schulman, 2015, NIPS 2015.

Decoupled Neural Interfaces using Synthetic Gradients pdf

Alex Graves, Max Jaderberg, Simon Osindero, 2016, ICML.

Self-Tuning Deep Reinforcement Learning pdf

Junhyuk Oh, Satinder Singh, David Silver, 2020, ArXiv.

A regional point descriptor for global topological localization in flooded subterranean environments

David M. Bradley, David Silver, Scott Thayer, 2004, IEEE Conference on Robotics, Automation and Mechatronics, 2004..

Mastering Atari, Go, chess and shogi by planning with a learned model

Demis Hassabis, Karen Simonyan, David Silver, 2019, Nature.

Arc carving: obtaining accurate, low latency maps from ultrasonic range sensors

Howie Choset, Ioannis M. Rekleitis, David Silver, 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

Unsupervised Predictive Memory in a Goal-Directed Agent pdf

Joel Z. Leibo, Demis Hassabis, Koray Kavukcuoglu, 2018, ArXiv.

Actor-Critic Reinforcement Learning with Energy-Based Policies

Yee Whye Teh, David Silver, Nicolas Heess, 2012, EWRL.

Cooperative Pathfinding

David Silver, David Silver, 2005, AIIDE.

Bayesian Optimization in AlphaGo pdf

Nando de Freitas, David Silver, Ziyu Wang, 2018, ArXiv.

Scalable and Efficient Bayes-Adaptive Reinforcement Learning Based on Monte-Carlo Tree Search

Peter Dayan, David Silver, Arthur Guez, 2013, J. Artif. Intell. Res..

Learning for Autonomous Navigation

David M. Bradley, David Silver, J. Andrew Bagnell, 2010, IEEE Robotics & Automation Magazine.

Authenticity, Relatability and Collaborative Approaches to Sharing Knowledge about Assistive Living Technology

Patrick Olivier, Peter C. Wright, David Silver, 2015, CSCW.

Human-level control through deep reinforcement learning

Shane Legg, Alex Graves, Marc G. Bellemare, 2015, Nature.

Expected Eligibility Traces pdf

David Silver, Matteo Hessel, Hado van Hasselt, 2020, ArXiv.

Learning to Search with MCTSnets

Rémi Munos, Oriol Vinyals, Karen Simonyan, 2018, ICML.

Deep Reinforcement Learning with Double Q-Learning pdf

David Silver, Arthur Guez, Hado van Hasselt, 2015, AAAI.

Successor Features for Transfer in Reinforcement Learning

Tom Schaul, David Silver, Jonathan J. Hunt, 2016, NIPS.

High Performance Outdoor Navigation from Overhead Data using Imitation Learning

David Silver, J. Andrew Bagnell, Anthony Stentz, 2008, Robotics: Science and Systems.

Meta-Gradient Reinforcement Learning

David Silver, Zhongwen Xu, Hado van Hasselt, 2018, NeurIPS.

Move Evaluation in Go Using Deep Convolutional Neural Networks pdf

David Silver, Ilya Sutskever, Chris J. Maddison, 2014, ICLR.

Monte-Carlo tree search and rapid action value estimation in computer Go

David Silver, Sylvain Gelly, D. Silver, 2011, Artif. Intell..

Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement pdf

Tom Schaul, Rémi Munos, David Silver, 2018, ICML.

Emergence of Locomotion Behaviours in Rich Environments pdf

Yuval Tassa, Martin A. Riedmiller, David Silver, 2017, ArXiv.

An investigation of model-free planning pdf

Tom Eccles, Laurent Orseau, David Silver, 2019, ICML.

Credit Assignment Techniques in Stochastic Computation Graphs

David Silver, Nicolas Heess, Lars Buesing, 2019, AISTATS.

Applied Imitation Learning for Autonomous Navigation in Complex Natural Terrain

David Silver, J. Andrew Bagnell, Anthony Stentz, 2009, FSR.

Unit Tests for Stochastic Optimization pdf

Tom Schaul, David Silver, Ioannis Antonoglou, 2013, ICLR.

The Value-Improvement Path: Towards Better Representations for Reinforcement Learning pdf

Marc G. Bellemare, David Silver, Will Dabney, 2020, ArXiv.

Prioritized Experience Replay pdf

Tom Schaul, David Silver, Ioannis Antonoglou, 2015, ICLR.

Learning values across many orders of magnitude

David Silver, Matteo Hessel, Arthur Guez, 2016, NIPS.

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm pdf

Demis Hassabis, Karen Simonyan, David Silver, 2017, ArXiv.

The Value Equivalence Principle for Model-Based Reinforcement Learning pdf

Satinder Singh, David Silver, Andr'e Barreto, 2020, NeurIPS.

The hierarchical atlas

Howie Choset, Ioannis M. Rekleitis, David Silver, 2005, IEEE Transactions on Robotics.

Recent developments in subterranean robotics

William Whittaker, David M. Bradley, David Silver, 2006, J. Field Robotics.

Topological Global Localization for Subterranean Voids

David Silver, Scott Thayer, Joseph Carsten, 2005, FSR.

Deterministic Policy Gradient Algorithms

Guy Lever, Martin A. Riedmiller, David Silver, 2014, ICML.

Combining Online and Offline Learning in UCT

David Silver, S Gelly, D. Silver, 2007 .

Playing Games with Language in a Monte-Carlo framework

Regina Barzilay, David Silver, Srk Branavan, 2011, Annual Meeting of the Association for Computational Linguistics.

Deep Reinforcement Learning from Self-Play in Imperfect-Information Games pdf

David Silver, Johannes Heinrich, D. Silver, 2016, ArXiv.

Behaviour Suite for Reinforcement Learning pdf

Tor Lattimore, Richard S. Sutton, Csaba Szepesvári, 2019, ICLR.

The Predictron: End-To-End Learning and Planning pdf

Tom Schaul, David Silver, Matteo Hessel, 2016, ICML.

Universal Value Function Approximators

Tom Schaul, David Silver, Karol Gregor, 2015, ICML.

StarCraft II: A New Challenge for Reinforcement Learning pdf

Tom Schaul, Oriol Vinyals, Karen Simonyan, 2017, ArXiv.

Sample-based learning and search with permanent and transient memories

Richard S. Sutton, David Silver, Martin Müller, 2008, ICML '08.

A Monte-Carlo AIXI Approximation pdf

Joel Veness, Marcus Hutter, David Silver, 2009, J. Artif. Intell. Res..

Asynchronous Methods for Deep Reinforcement Learning pdf

Alex Graves, Koray Kavukcuoglu, David Silver, 2016, ICML.

Universal Successor Features Approximators pdf

Tom Schaul, David Silver, Hado van Hasselt, 2018, ICLR.

Feature extraction for topological mine maps

David Silver, Dave Ferguson, Scott Thayer, 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

David Silver, Thore Graepel, Karl Tuyls, 2017, NIPS.

Meta-Gradient Reinforcement Learning with an Objective Discovered Online pdf

Junhyuk Oh, Satinder Singh, David Silver, 2020, NeurIPS.

Reinforcement learning and simulation-based search in computer go

David Silver, D. Silver, David Silver, 2009 .

Learning to Win by Reading Manuals in a Monte-Carlo Framework

Regina Barzilay, David Silver, S. R. K. Branavan, 2011, ACL.

On the role of tracking in stationary environments

Richard S. Sutton, David Silver, Anna Koop, 2007, ICML '07.

Learning functions across many orders of magnitudes pdf

David Silver, Matteo Hessel, Arthur Guez, 2016, ArXiv.

Learning Continuous Control Policies by Stochastic Value Gradients

Yuval Tassa, David Silver, Nicolas Heess, 2015, NIPS.

Playing Atari with Deep Reinforcement Learning pdf

Alex Graves, Koray Kavukcuoglu, Martin A. Riedmiller, 2013, ArXiv.

Active learning from demonstration for robust autonomous navigation

David Silver, J. Andrew Bagnell, Anthony Stentz, 2012, 2012 IEEE International Conference on Robotics and Automation.

Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13)

Pushmeet Kohli, Demis Hassabis, Karen Simonyan, 2019, Proteins.

Perceptual Interpretation for Autonomous Navigation through Dynamic Imitation Learning

David Silver, J. Andrew Bagnell, Anthony Stentz, 2009, ISRR.

Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (2008) Achieving Master Level Play in 9 × 9 Computer Go

David Silver, Sylvain Gelly, D. Silver, 2022 .

Monte-Carlo simulation balancing

Gerald Tesauro, David Silver, G. Tesauro, 2009, ICML '09.

Book Review: Life Online: Researching Real Experience in Virtual Space

David Silver, D. Silver, 2000, New Media Soc..

Gradient Temporal Difference Networks

David Silver, David Silver, 2012, EWRL.

Distributed Prioritized Experience Replay pdf

David Budden, David Silver, Matteo Hessel, 2018, ICLR.

Massively Parallel Methods for Deep Reinforcement Learning pdf

Shane Legg, Praveen Srinivasan, Koray Kavukcuoglu, 2015, ArXiv.

A Self-Tuning Actor-Critic Algorithm

Junhyuk Oh, Satinder Singh, David Silver, 2020, NeurIPS.

Rainbow: Combining Improvements in Deep Reinforcement Learning

Tom Schaul, David Silver, Matteo Hessel, 2017, AAAI.

Value-driven Hindsight Modelling pdf

Fabio Viola, Doina Precup, David Silver, 2020, NeurIPS.

Human-level performance in 3D multiplayer games with population-based reinforcement learning

Guy Lever, Joel Z. Leibo, Demis Hassabis, 2018, Science.

FeUdal Networks for Hierarchical Reinforcement Learning

Tom Schaul, Max Jaderberg, Simon Osindero, 2017, ICML.

Learning to search: Functional gradient techniques for imitation learning

David Silver, J. Andrew Bagnell, Nathan D. Ratliff, 2009, Auton. Robots.

Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain

David Silver, J. Andrew Bagnell, Anthony Stentz, 2010, Int. J. Robotics Res..

Fast gradient-descent methods for temporal-difference learning with linear function approximation

Shalabh Bhatnagar, Doina Precup, Richard S. Sutton, 2009, ICML '09.

Mastering the game of Go without human knowledge

Demis Hassabis, Karen Simonyan, Fan Hui, 2017, Nature.

Bayes-Adaptive Simulation-based Search with Value Function Approximation

Peter Dayan, David Silver, Nicolas Heess, 2014, NIPS.

Learning Autonomous Driving Styles and Maneuvers from Expert Demonstration

David Silver, J. Andrew Bagnell, Anthony Stentz, 2012, ISER.

Digital natives on a media fast

David Silver, David Silver, 2012, Inf. Serv. Use.

Combining online and offline knowledge in UCT

David Silver, Sylvain Gelly, D. Silver, 2007, ICML '07.

LADAR-Based Pipeline Inspection and Location

David Silver, Nicolas Vandapel, Scott M. Thayer, 2007 .

Value Iteration with Options and State Aggregation pdf

David Silver, Kamil Ciosek, D. Silver, 2015, ArXiv.

Bootstrapping from Game Tree Search

Joel Veness, David Silver, William T. B. Uther, 2009, NIPS.

Mastering the game of Go with deep neural networks and tree search

Demis Hassabis, Koray Kavukcuoglu, David Silver, 2016, Nature.

Implicit Quantile Networks for Distributional Reinforcement Learning pdf

Rémi Munos, David Silver, Georg Ostrovski, 2018, ICML.

Monte Carlo Localization and registration to prior data for outdoor navigation

David Silver, Anthony Stentz, 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Discovering Reinforcement Learning Algorithms pdf

Junhyuk Oh, Satinder Singh, David Silver, 2020, NeurIPS.

Introduction to the special issue on deep reinforcement learning: An editorial

Gerald Tesauro, David Silver, Guang-Bin Huang, 2018, Neural Networks.

Learning and Planning in Complex Action Spaces pdf

David Silver, Julian Schrittwieser, Ioannis Antonoglou, 2021, ICML.

Reward is enough

Doina Precup, David Silver, Satinder Singh, 2021, Artif. Intell..

Online and Offline Reinforcement Learning by Planning with a Learned Model pdf

David Silver, Julian Schrittwieser, Ioannis Antonoglou, 2021, NeurIPS.

Muesli: Combining Improvements in Policy Optimization pdf

David Silver, Matteo Hessel, Fabio Viola, 2021, ICML.

Proper Value Equivalence pdf

Christopher Grimm, David Silver, Gregory Farquhar, 2021, NeurIPS.

Discovery of Options via Meta-Learned Subgoals pdf

Junhyuk Oh, David Silver, Matteo Hessel, 2021, NeurIPS.

Bootstrapped Meta-Learning pdf

David Silver, Tom Zahavy, Yannick Schroecker, 2021, ArXiv.

Self-Consistent Models and Values pdf

Zita Marinho, David Silver, Matteo Hessel, 2021, NeurIPS.

The Value-Improvement Path: Towards Better Representations for Reinforcement Learning pdf

Marc G. Bellemare, David Silver, Mark Rowland, 2020, AAAI.

Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13)

Pushmeet Kohli, Demis Hassabis, Karen Simonyan, 2019, Proteins.

Value-driven Hindsight Modelling pdf

Doina Precup, David Silver, Nicolas Heess, 2020, NeurIPS.